In the following section, we present two novel algorithms, which recursively remove features in logarithmic steps. Instead of removing attributes in constant intervals, the algorithms attempt to optimize resource usage by removing larger subsets of the lowest ranked features and analyzing in more detail the most promising feature subsets. In this respect, the proposed methods share the motivation of Bayesian optimization methods and the Auto ML movement [1]}, [2]}, [3]}, [4]}. However, Auto ML algorithms are general purpose methods used mostly for classifier tuning, which do not take into account the fact that performing a removal step in feature elimination affects the results for consecutive steps.
